Tag: chatgpt

The US Must Own Its AI Future

The U.S. has an opportunity to lead the world in AI, but only if we make the right choices now. The federal government must mandate open-source AI adoption across defense, intelligence, and public services, ensuring that the code that runs our country belongs to the people, not private corporations.

If the government doesn’t control its AI, then the AI will control the government

By Joe Merrill, CEO, OpenTeams

Artificial intelligence is reshaping national security, defense, and public infrastructure at a breathtaking pace. Governments around the world are deploying AI to modernize operations, improve intelligence capabilities, and optimize decision-making. But there’s a fundamental question that the U.S. must answer now: Who should control the AI that powers our government?

The recent White House call for an AI Action Plan under Executive Order 14179, Removing Barriers to American Leadership in Artificial Intelligence (January 23, 2025) calls for a united commitment to removing regulatory obstacles, fostering private sector innovation, and maintaining U.S. global leadership in AI. However, we believe one fundamental principle must guide AI adoption in government: the U.S. must own, control, and understand the AI that powers our national infrastructure. 

This means prioritizing open source AI, where the code is transparent, auditable, and government controlled over proprietary, black box systems managed by private companies with competing interests and opaque security risks.

We’ve already seen how relying on black box AI systems can backfire:

  1. The Pentagon’s JEDI Cloud Failure – The Department of Defense originally awarded a $10 billion contract for a proprietary AI powered cloud system. But after years of delays, lawsuits, and concerns about vendor lock-in, the project was scrapped in favor of a multi-vendor approach that embraced open architectures.
  2. The VA’s AI Powered Scheduling Debacle – The Department of Veterans Affairs attempted to modernize patient scheduling using proprietary AI software. The result? Technical failures, mismanagement, and a $2.5 billion project collapse that left veterans waiting longer than ever.
  3. Police Departments and AI Bias – Cities across the U.S. have adopted closed AI facial recognition tools that have been shown to misidentify minorities at alarming rates. Without open access to the algorithms, the government has little ability to audit, correct, or improve these systems.

The European Union has begun passing laws banning government use of black-box AI in critical applications, recognizing that if the government doesn’t control its AI, then AI controls the government. The U.S. must follow suit.

Here’s five reasons why America needs to wake up immediately and fix this urgently:

  1. National Security & Sovereignty – AI should be controlled by the U.S. government, not private companies with financial interests or foreign ties. Open source AI ensures that we own and understand the technology that powers our defense, intelligence, and critical infrastructure.
  2. Transparency & Accountability – Unlike proprietary AI, open source models allow the public and independent experts to audit decisions, reduce bias, and prevent hidden agendas.
  3. Cost Efficiency – The government shouldn’t pay licensing fees to private AI vendors indefinitely. Open source AI eliminates vendor lock-in, reducing costs over time and allowing the government to invest in internal AI expertise.
  4. Interoperability & Innovation – Open systems integrate with existing technology and foster innovation by allowing agencies to build on top of each other’s work rather than reinventing the wheel.
  5. Public Trust – When AI makes decisions that affect millions of Americans, from loan approvals to prison sentencing, citizens deserve to know how those decisions are made. Open source AI enables transparency and accountability.

The U.S. has an opportunity to lead the world in AI, but only if we make the right choices now. The federal government must mandate open source AI adoption across defense, intelligence, and public services, ensuring that the code that runs our country belongs to the people, not private corporations.

As the White House moves forward with its AI Action Plan, open source AI must be a central pillar of America’s strategy. National security, economic independence, and democratic accountability depend on it. 

The future of AI should be open. Let’s make it happen.

The Shocking Economics of AI

AI training and deploying companies are trying to capture your data. An understanding of the industry economics can help you avoid this pitfall.

Artificial Intelligence (AI) is often framed as the domain of a few elite companies. But a closer look reveals a massive gap between those who actually build AI and those who put wrappers on it, train it, and resell it.

The actual numbers for who builds AI are shockingly small. Only a small number of companies, primarily five, contribute to open-source AI at scale: Meta, Google, Nvidia, Microsoft, and OpenTeams. In saying so, I also want to point out smaller and significant contributions from companies like Stability AI, EleutherAI, and Mistral, who all make meaningful code updates and changes despite their smaller size.

Because open source AI is free to download doesn’t mean it is free to operate. Like adopting a puppy, you still have non-trivial startup costs and maintenance. However, these expenses are not what industry developers have traditionally suggested and are shockingly affordable for most enterprises.

These economic factors combine to produce an AI industry with barriers ironically related to human development and not compute.  Just as the best time to plant a tree is 20 years ago, the best time to create new AI developers was a long time ago. It will take a generation to produce adequate supply for the field due to lengthy training requiring PhD level know-how in mathematics, statistics, and computer science plus years of coding experience.

On the technical input side of the AI building equation, training costs are collapsing and with an increasingly high-quality library of pre-trained, open source models already available, everyone can have their own AI. Indeed, those who don’t will be at the mercy of those who do.

Shareholders and voters will not buy the story that giving away their competitive and personal data to AI oligarchs is economically necessary or valuable. Communal LLMs are economically inefficient, epistemically noisy, and strategically reckless for any enterprise or government that values performance, accuracy, or control of its intellectual property. The privacy risks inherent in communal models are real. The future of AI will be enterprises owning their open source AI to save costs, provide accurate answers, and protect their intellectual property. This is the efficient market solution preferred to communal LLMs sucking massive power to untangle spurious correlations from superfluous data.

To be sure, black-box AI SaaS providers will still have a business model as “drivers” of AI as opposed to those who build and deploy it. However, that economic pie will be smaller just as F1 drivers still do financially well, but nowhere near the economic returns earned by those who build cars. The upstream builders of AI will always have an advantage when deploying and training models because of the advanced know-how they possess to optimize AI and fully take advantage of its capabilities.

The AI Industry Pyramid

Below is a visual representation of the AI workforce, from elite maintainers to the broader ecosystem of users:

Detailed Platform Stats

1. PyTorch
– GitHub contributors: ~3,900+
– Monthly active contributors: ~250
– Core Maintainers: 80
– Users: ~4–5 million
– Lead Developer: Meta AI
– Source: https://github.com/pytorch/pytorch

2. TensorFlow
– GitHub contributors: ~3,500+
– Monthly active contributors: ~200
– Core Maintainers: 70
-Users: ~2.5–3.5 million
– Lead Developer: Google
– Source: https://github.com/tensorflow/tensorflow

3. Hugging Face
– GitHub contributors: ~2,600+ (Transformers repo)
– Monthly active contributors: ~150
-Core Maintainers: 50
– Users: ~1.5–2.5 million
– Source: https://github.com/huggingface/transformers

Global Ecosystem of Users

AI/ML Engineers (1.2M – 1.8M): Full-time professionals training and deploying models using programs provided by open source
Academic Users (800K – 1.2M): Researchers, grad students, and educators who also train and deploy models
Indie Devs & Hobbyists (400K – 700K): Kaggle participants, open-source users, startup tinkerers
Enterprise Software Devs (3M – 5M): Developers integrating AI into enterprise apps

Industry Impact

• 75%+ of Fortune 500 companies use open-source AI directly or via cloud providers
• Hugging Face Hub sees millions of downloads monthly (https://huggingface.co)
• GitHub’s Octoverse consistently ranks PyTorch and TensorFlow among the top OSS projects (https://octoverse.github.com/)

Top AI Companies: Builders vs. Train & Deployers

Open Source Corporate Builders:
• Meta (PyTorch, Llama)
• Google (TensorFlow, JAX)
• Microsoft (ONNX, DeepSpeed)
• NVIDIA (CUDA, cuDNN, Triton)
• OpenTeams (Nebari, PyTorch, TensorFlow)
Note: these companies can also train and deploy models

Open Source Train & Deploy Companies:
• Amazon, Apple, Salesforce, Oracle, Palantir, Snowflake, Databricks, C3.ai, SAP, OpenAI, Anthropic, xAI
Note: both Mistral and Anthropic claim to have some internal building capabilities, but as closed-box systems it cannot be confirmed

The Collapsing Cost of Training

Availability and Complexity of Open Source LLMs

Sources

• PyTorch contributors: https://github.com/pytorch/pytorch
• TensorFlow contributors: https://github.com/tensorflow/tensorflow
• Hugging Face contributors: https://github.com/huggingface/transformers
• Developer counts from GitHub Octoverse, Stack Overflow Developer Survey
• Enterprise stats from AWS, Azure, GCP, LinkedIn data